GREEN SYNTHESIS OF METAL-ORGANIC FRAMEWORKS (MOFS) FOR INDUSTRIAL WASTEWATER REMEDIATION IN PAKISTAN

Abstract

Industrial wastewater contamination has become a critical environmental challenge, particularly in developing countries such as Pakistan, where untreated effluents from textile, leather, pharmaceutical, and chemical industries significantly degrade water quality. Conventional wastewater treatment technologies are often inefficient in removing complex pollutants such as heavy metals, dyes, and emerging organic contaminants, while also generating secondary pollution and requiring high operational costs. In this context, Metal–Organic Frameworks (MOFs) have emerged as advanced porous materials with exceptional adsorption capacity, tunable pore structures, and high surface area, making them highly effective for wastewater remediation. This study investigated the application of green-synthesized MOFs for industrial wastewater treatment, emphasizing sustainable synthesis routes and enhanced environmental performance. MOFs were synthesized using eco-friendly methods and evaluated for their adsorption efficiency, kinetic behavior, and regeneration capability in removing heavy metals and organic pollutants from industrial wastewater samples. The performance was compared with conventional MOFs and activated carbon under controlled laboratory conditions. The results demonstrated that green-synthesized MOFs significantly outperformed conventional adsorbents, achieving higher removal efficiencies, superior adsorption capacities, and improved reusability. Kinetic and isotherm analyses indicated that chemisorption and monolayer adsorption dominated the removal process. The findings confirm that green synthesis not only enhances environmental sustainability but also improves functional performance of MOFs in wastewater treatment applications.

Overall, the study concludes that green-synthesized MOFs represent a promising, scalable, and environmentally friendly solution for industrial wastewater remediation, particularly in resource-constrained regions.